An image with no faces in it was indistinguishable from one that had
never been looked at, so every landscape, still life and document scan in
the library was re-detected on every pass, for ever. In a real library
that is most of it: on the 23,527-image test library, 64 of the first 110
images indexed contain no face at all.
Schema v9 adds face_index, a run marker per (image, model) carrying the
face count and the proxy edge it read. Keyed on the model, so a model
change puts every image back in the queue by itself.
That makes a coverage figure possible, which is the thing a user actually
wants to see. The audit also splits the outstanding set by whether a
proxy exists, because 23,417 awaiting a proxy and 110 ready to index are
different problems, and telling the user to run indexing again would not
fix the first.
The Identity screen gains Index faces, Stop, and the coverage line.
examples/face_index.rs is the same check and sweep without a window,
which is the right shape for an overnight pass.
Measured on the real library in release: 3.5 images/second, 110 images
and 125 faces in 30 seconds, and a second run correctly finds nothing
left to do.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
FR-CULL-9 forbids thresholding a bare cosine anywhere in the subsystem,
so calibrate fits P(same person) per library and reports whether the fit
is trustworthy. Two details carry most of the weight.
The fit runs against a 200-bin histogram rather than a pair list: a
25,000-face library has ~3e8 pairs and no gradient descent is running
over that. And a fresh library has no valid calibration, because the
positives have to come from user confirmations or burst siblings --
bootstrapping them from high cosine would fit the calibration to the
belief it was supposed to test.
Clustering defends against the over-merging FR-CULL-10 warns about with
constraints rather than a better threshold: two faces in one photograph
never merge, and two groups confirmed as different people never merge.
Average link rather than single link, so one strong edge cannot weld two
families together.
Calibration is defined once, in dr-face, and dr-catalog re-exports it.
Two implementations of one probability model is exactly how a number
comes to mean the wrong thing.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Schema v8: people, faces, face_person, face_person_rejected, and the
per-library calibration. Follows catalog.md 10.1 with two additions the
spec work turned up.
crop_px, because at the 1024px proxy tier a group shot reaches the
embedder at ~50 source pixels upsampled to 112 and a portrait at 340.
FR-CULL-9 names face size as an axis along which an uncalibrated
similarity misbehaves, so it is a stored feature rather than a UI hint.
face_person_rejected, because rejection is not the absence of an
assignment. Without it the next clustering pass re-suggests exactly the
face the user just pushed away, and the tool feels broken.
record_detections replaces rather than appends, since DetectFaces is
coalesced per image -- and carries confirmations across the replacement
by box overlap, so re-indexing with a better model cannot discard the
user's own labelling.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
dtourolleandClaude Opus 5